Alipio Jorge

Also published as: Alípio Jorge


2019

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LIAAD at SemDeep-5 Challenge: Word-in-Context (WiC)
Daniel Loureiro | Alípio Jorge
Proceedings of the 5th Workshop on Semantic Deep Learning (SemDeep-5)

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Language Modelling Makes Sense: Propagating Representations through WordNet for Full-Coverage Word Sense Disambiguation
Daniel Loureiro | Alípio Jorge
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Contextual embeddings represent a new generation of semantic representations learned from Neural Language Modelling (NLM) that addresses the issue of meaning conflation hampering traditional word embeddings. In this work, we show that contextual embeddings can be used to achieve unprecedented gains in Word Sense Disambiguation (WSD) tasks. Our approach focuses on creating sense-level embeddings with full-coverage of WordNet, and without recourse to explicit knowledge of sense distributions or task-specific modelling. As a result, a simple Nearest Neighbors (k-NN) method using our representations is able to consistently surpass the performance of previous systems using powerful neural sequencing models. We also analyse the robustness of our approach when ignoring part-of-speech and lemma features, requiring disambiguation against the full sense inventory, and revealing shortcomings to be improved. Finally, we explore applications of our sense embeddings for concept-level analyses of contextual embeddings and their respective NLMs.

2018

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Affordance Extraction and Inference based on Semantic Role Labeling
Daniel Loureiro | Alípio Jorge
Proceedings of the First Workshop on Fact Extraction and VERification (FEVER)

Common-sense reasoning is becoming increasingly important for the advancement of Natural Language Processing. While word embeddings have been very successful, they cannot explain which aspects of ‘coffee’ and ‘tea’ make them similar, or how they could be related to ‘shop’. In this paper, we propose an explicit word representation that builds upon the Distributional Hypothesis to represent meaning from semantic roles, and allow inference of relations from their meshing, as supported by the affordance-based Indexical Hypothesis. We find that our model improves the state-of-the-art on unsupervised word similarity tasks while allowing for direct inference of new relations from the same vector space.

2010

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D-Confidence: An Active Learning Strategy which Efficiently Identifies Small Classes
Nuno Escudeiro | Alípio Jorge
Proceedings of the NAACL HLT 2010 Workshop on Active Learning for Natural Language Processing